With the advent of security and surveillance systems, the need to accurately track objects in images and videos has increased multi-fold. Be it home security or any other premise monitoring (such as public areas, roads, official buildings, schools, or any other establishments), it is imperative to perform optimized and precise video analytics (such as detecting and tracking objects). An object-to-be-tracked could range from a person, vehicle, animal, building, an article to any other similar object. Further, since video analytics help extract meaningful insights from images or video grabs, it also finds utility in other domains such as a retail system, a monitoring system, a business intelligence-based system, and the like. For example, in a retail system, video analytics tools are used to track customers or carts inside a retail store/mall, or to monitor customer wait times. Similarly, for business-intelligence based systems, video analytics is used to measure traffic patterns and open/close business performance at multiple commercial locations.
The existing video analytics solutions perform object tracking based on at least one of a MeanShift technique, an Optical Flow technique, and more recently online learning based strategies. In online learning based strategies, the common theme is to continuously learn and update a discriminative classifier model, such as Support Vector Machine (SVM), which attempts to learn the separation of the object from its surroundings. Given this model at a time interval t−1, location of an object at time interval t can be predicted. However, a major shortcoming of such solutions is their computational complexity, because of which these are not suitable to be implemented on embedded platforms. Therefore, there is a need for an accurate and computationally efficient solution for solving the problem of object tracking in videos/images.